Apparatus and method for effect pigment identification

ABSTRACT

A computer-implemented method for identifying an effect pigment, the method comprising executing, on at least one processor of at least one computer, steps of: a) acquiring sample image data describing a digital image of a layer comprising a sample effect pigment b) determining, based on the sample image data, sparkle point data describing a sample distribution of sparkle points defined by the digital image, wherein the sample distribution is defined in an N-dimensional color space, wherein N is an integer value equal to or larger than 3; c) determining, based on the sparkle point data, sparkle point transformation data describing a transformation of the sample distribution into an (N−1)-dimensional color space; d) determining, based on the sparkle point transformation data, sparkle point distribution geometry data describing a geometry of the sample distribution; e) acquiring reference distribution geometry data describing a geometry of a reference distribution of sparkle points in the (N−1)-dimensional color space; f) acquiring reference distribution association data describing an association between the reference distribution and an identifier of the reference distribution; g) determining, based on the sparkle point distribution geometry data and the reference distribution geometry data and the reference distribution association data, sample pigment identity data describing an identity of the sample effect pigment.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a National Stage Entry of InternationalPatent Application serial number PCT/EP17/71395, filed Aug. 25, 2017,entitled “APPARATUS AND METHOD FOR EFFECT PIGMENT IDENTIFICATION,”currently expired; which claims priority from Provisional ApplicationSer. No. 62/382,813, filed Sep. 2, 2016, entitled “APPARATUS AND METHODFOR EFFECT PIGMENT IDENTIFICATION”.

FIELD OF THE INVENTION

The present disclosure relates to the identification of effect pigmentsin layers.

BACKGROUND

Comparing or matching colors of surfaces coated with paints containingeffect pigments is challenging both visually or using color measurementdevices. Such paints are for example often seen on cars. A core problemderives from the fact that effect pigments form very bright spots withchanging colors depending on viewing and illumination angle. Automaticor human-assisted identification of a coating containing effect pigmentsmay therefore require a large number of measurements, data processingthat is adapted to coatings containing effect pigments, and effectpigment classification and identification methods. Although some devicesmay provide information based on broad imaging of coatings containingeffect pigments, they may fail to provide the effect pigment-levelidentification that may be needed for the precise analysis required toformulate the ingredients needed to produce a corresponding coating.

Efficient matching of gonioapparent color shades of undisclosedpigmentation is a major and time-consuming task in various areas ofapplication of coatings industry. This task is particularly challengingin those cases, where color standards contain effect pigments such asplatelet-like pearlescent (interference) or metallic particle types.

In order to keep the number of combinations in a combinatorial searchfor a matching formulation tractable it is necessary to identify as manypigments as possible in advance so that they are an integral part of allformulations tested in the computational process. If, e. g., all effectpigments used in a color target can be identified in advance, only thesolid pigments of the formulation need to be determined in thecombinatorial search for the best matching formula, which will result ina dramatic saving in compute time.

Identification of effect pigments in surface coatings by traditionalvisual assessment under a spot light source is a challenging taskrequiring well-controlled viewing conditions and many years ofexperience in color development. The appearance of gonioapparent surfacecoatings is determined by complex interaction of color and visualtexture. The latter is an appearance feature of effect pigments anddescribes the observed non-homogeneity of color and/or intensitydistribution at the surface of a gonioapparent material. Characteristicsparkle of effect pigments depends on viewing with directionalillumination. When viewed in diffuse illumination the sparkle of acoating virtually disappears and gives way to an irregular lightnesspattern, characteristic for the grade of an effect pigment, itsmorphological properties or/and the particle topology within a coating.The latter can be influenced by adding a flop control agent to a paintformulation or by application conditions.

Flake appearance of effect pigments depends on just a few visualfeatures so there is relatively little information for classifying them.The sample appearance also depends on lightness, color and otherpigments in an effect color coating so that it is difficult to compare.A typical recipe for an effect color shade is a blend of two or moreeffect pigments and three or more transparent or semi-transparent solidpigments. Determining the composite visual appearance created by two ormore effect pigments from reference samples of single flakes is noteffective, in particular if the color of sample coating and referencediffer (for example, comparison of a flake reference in a silver colorto a blue metallic/pearlescent test color). An inherent limitation ofvisual assessment of gonioapparent surface coatings with the goal ofidentification of effect pigments is redundancy.

Compared to a mere visual assessment of surface coatings, the method ofoptical microscopy provides better flake discrimination.

This method of optical microscopy of effect color shades is frequentlyused in the color matching process in automotive industry. The lateralextension of platelet-like effect pigments, typically ranging between 10to 50 μm, enables such an application. The resolution limit of opticalmicroscopy, which is of the order of about 1 μm, is well below thetypical size of special effect pigments so that they can easily beidentified in a surface coating, when magnified appropriately.

Microscope imaging allows direct comparison of pigment micro featuresinstead of comparison of appearances which depends on other factors. Inthis method in its most elementary approach a comparison is made bymicroscopic observation, or images, of the coated surface in question,with either reference samples, images of reference samples, or featuresderived from images of reference samples. Optical microscopy of surfacecoatings allows a direct comparison of micro features instead ofcomparison of appearances and identification of effect pigments. This isachieved by virtually detecting effect pigment properties such as size,shape, edge and surface morphology, and color.

Therefore, there is a need for an apparatus and method forcharacterizing effect pigment spots in images of layers.

SUMMARY OF THE INVENTION

Embodiments of the present disclosure provide, according to a firstexample of a first aspect, an apparatus and method for characterizingeffect pigment spots in images of layers, the method comprising thesteps: forming one or more point clouds of effect pigment spots, eachpoint in the point clouds being characterized by at least threedevice-independent color coordinates of an effect pigment spot: a firstand a second linearly projected chromaticity coordinates, and alightness-related value; computing the distance of one or more pointclouds to one or more reference point clouds; and selecting thereference point cloud with shortest distance.

Furthermore in the method of the present disclosure according to thefirst example of the first aspect, forming one or more point clouds maycomprise forming one or more point cloud clusters. Computing thedistance may comprise encoding the one or more point clouds into ahierarchical tree data structure. Computing the distance may compriseforming a point cloud envelope. Computing the distance may comprisecomputing the cloud's centroid projected on a plane of a chromaticitydiagram. Computing the distance may comprise computing a measure of thecloud's statistical dispersion. Computing the distance may comprise thesteps: computing the lightness of one or more effect pigment spotswithin one or more images; computing a lightness mean of effect pigmentspots; computing a deviation from lightness mean of the effect pigmentspots; and wherein forming one or more point clouds of effect pigmentspots, the lightness equals the deviation from lightness mean. At leasta first and a second linearly projected chromaticity coordinates may becomputed according to the CIE 1976 standard. The value of one or more ofthe device-independent color coordinates may be corrected by a colorvalue measured over a surface the size of which corresponds to that ofat least nine effect pigment spots. The method may further comprise astep of comparing the point cloud to one or more reference point clouds.The images comprising effect pigment spots may be formed by applying animage processing method to the images of a layer comprising effectpigments. The images comprising effect pigment spots may be formed byapplying a thresholding method to the images of a layer comprisingeffect pigments. Forming one or more point clouds may comprise a pointclustering method. Forming one or more point clouds may comprise a stepof automatically comparing the point cloud to one or more referencepoint clouds with a two- or more-dimensional pattern matching method.The images may share a common color calibration basis. The images may beacquired by a digital color camera. The images may be formed fromcombining a plurality of images acquired at different exposures. Theimages may be acquired at a plurality of viewing and illumination anglecombinations. The method may further comprise a step of automaticallycomparing a plurality of point clouds acquired at a plurality of viewingand illumination angle combinations to one or more reference pointclouds with a three-dimensional pattern matching method. The pointclouds may be displayed on a plot comprising three or more axes, whereina first axis is for the first chromaticity coordinate u′, a second axisis for the second chromaticity coordinate v′, and a third axis is forthe deviation from lightness mean (L*−<L*>) of the effect pigment spots.The centroid of the point cloud along one or more dimensions oflightness or color-opponent dimensions may be computed for one or moreillumination-observation angle combination. A dataset comprising one ormore centroids of the point cloud along one or more dimensions oflightness or color-opponent dimensions may be provided to acomputer-based system.

Furthermore, the present disclosure provides, according to a secondexample of a second aspect, a method for characterizing effect pigmentspots in images of layers, the method comprising: providing one or moredigital cameras; providing one or more illumination sources; providingone or more electronic displays; providing a plurality of referencepoint clouds stored in non-volatile semiconductor memory; acquiringcolor image data from the digital cameras; forming one or more pointclouds of effect pigment spots, each point in the point clouds beingcharacterized by at least three device-independent color coordinates ofan effect pigment spot: a first and a second linearly projectedchromaticity coordinates, and a lightness-related value; computing thedistance of one or more point clouds to one or more reference pointclouds; and selecting the reference point cloud with shortest distance.

Furthermore, the present disclosure provides, according to a thirdexample of the first aspect, a database for characterizing effectpigment spots in images of layers and a method for forming such adatabase, the database comprising: images of a layer comprising effectpigments, said images comprising a plurality of effect pigment spotsformed within one or more images, each effect pigment spot characterizedby at least a first and a second linearly projected chromaticitycoordinates (u′, v′) and at least one lightness-related value L*; one ormore point clouds of effect pigment spots, each point in the pointclouds being characterized by device-independent color coordinates (u′,v′, L); a distance of one or more point clouds to one or more referencepoint clouds; and a selection of a reference point cloud with shortestdistance.

Furthermore, the present disclosure provides, in a fourth example of thefirst aspect, a computer-based layer analysis system for characterizingeffect pigment spots in images of layers, comprising: one or moredigital cameras; one or more illumination sources; one or moreelectronic displays; one or more central processing units; one or morenon-volatile semiconductor memories comprising a stored plurality ofreference point clouds; one or more non-volatile semiconductor memoriesstoring a program comprising instructions for execution by the centralprocessing unit for: acquiring color image data from the digitalcameras; forming one or more point clouds of effect pigment spots, eachpoint in the point clouds being characterized by at least threedevice-independent color coordinates of an effect pigment spot: a firstand a second linearly projected chromaticity coordinates, and alightness-related value; computing the distance of one or more pointclouds to one or more reference point clouds; and selecting thereference point cloud with shortest distance.

The examples according to the first aspect of this disclosure can bedefined by the following examples A to Y:

A. A method for characterizing effect pigment spots in images of layers,the method comprising the steps:

forming one or more point clouds of effect pigment spots, each point inthe point clouds being characterized by at least threedevice-independent color coordinates of an effect pigment spot:

-   -   a first and a second linearly projected chromaticity        coordinates, and    -   a lightness-related value;

computing the distance of one or more point clouds to one or morereference point clouds which are for example each associated with a(i.e. each one, for example each exactly one) reference effect pigment;and

selecting the reference point cloud with shortest distance, and forexample characterizing the effect pigment spot as the reference effectpigment based on the selected reference point cloud.

B. The method of example A, wherein forming one or more point cloudscomprises forming one or more point cloud clusters.

C. The method of any one of examples A to B, wherein computing thedistance comprises encoding the one or more point clouds into ahierarchical tree data structure.

D. The method of any one of example A to C, wherein computing thedistance comprises forming a point cloud envelope, for example a pointcloud of points representing the effect pigment spots.

E. The method of any one of example A to D, wherein computing thedistance comprises computing the centroid of the cloud projected on aplane of a chromaticity diagram.

F. The method of any one of examples A to E, wherein computing thedistance comprises computing a measure of the cloud's statisticaldispersion.

G. The method of any one of example A to F, wherein computing thedistance comprises the following steps:

computing the lightness of one or more effect pigment spots within oneor more images;

computing a lightness mean of effect pigment spots;

computing a deviation from lightness mean of the effect pigment spots;and

wherein forming one or more point clouds of effect pigment spots, thelightness equals the deviation from lightness mean.

H. The method of according to any one of examples A to G, wherein the atleast a first and a second linearly projected chromaticity coordinatesare computed according to the CIE 1976 standard.

I. The method of any one of examples A to H, wherein the value of one ormore of the device-independent color coordinates is corrected by a colorvalue measured over a surface the size of which corresponds to that ofat least 9 effect pigment spots.

J. The method of any one of examples A to I, wherein the method furthercomprises a step of comparing the point cloud to one or more referencepoint clouds.

K. The method of any one of examples A to J, wherein the imagescomprising effect pigment spots are formed by applying an imageprocessing method to the images of a layer comprising effect pigments.

L. The method of any one of examples A to K, wherein the imagescomprising effect pigment spots are formed by applying a thresholdingmethod to the images of a layer comprising effect pigments.

M. The method of any one of examples A to L, wherein the step of formingone or more point clouds comprises executing a point clustering method.

N. The method of any one of examples A to M, wherein the step of formingone or more point clouds comprises a step of automatically comparing thepoint cloud to one or more reference point clouds with a two- ormore-dimensional pattern matching method.

O. The method of any one of examples A to N, wherein the images share acommon color calibration basis.

P. The method of any one of examples A to O, wherein the images areacquired by a digital color camera.

Q. The method of any one of examples A to Q, wherein the images areformed from combining a plurality of images acquired at differentexposures.

R. The method of any one of examples A to Q, wherein the images areacquired at a plurality of viewing and illumination angle combinations.

S. The method of any one of examples A to R, wherein the method furthercomprises a step of automatically comparing a plurality of point cloudsacquired at a plurality of viewing and illumination angle combinationsto one or more reference point clouds with a three-dimensional patternmatching method.

T. The method of any one of examples A to S, wherein the point cloudsare displayed on a plot comprising three or more axes, wherein a firstaxis is for the first chromaticity coordinate u′, a second axis is forthe second chromaticity coordinate v′, and a third axis is for thedeviation from lightness mean (L*−<L*>) of the effect pigment spots.

U. The method of any one of examples A to T, wherein the centroid of thepoint cloud along one or more dimensions of lightness or color-opponentdimensions is computed for one or more illumination-observation anglecombination.

V. The method of any one of examples A to U, wherein a datasetcomprising one or more centroids of the point cloud along one or moredimensions of lightness or color-opponent dimensions is provided to acomputer-based system.

W. A method for characterizing effect pigment spots in images of layers,the method comprising:

-   -   providing one or more digital cameras;    -   providing one or more illumination sources;    -   providing one or more electronic displays;    -   providing a plurality of reference point clouds stored in        non-volatile semiconductor memory, each of the reference point        clouds being associated with a (i.e. each one, for example each        exactly one) reference effect pigment;    -   acquiring color image data from the digital cameras;    -   forming one or more point clouds of effect pigment spots, each        point in the point clouds being characterized by at least three        device-independent color coordinates of an effect pigment spot:    -   a first and a second linearly projected chromaticity        coordinates, and    -   a lightness-related value;        computing the distance of one or more point clouds to one or        more reference point clouds; and        selecting the reference point cloud with shortest distance, and        for example characterizing the effect pigment spot as the        reference effect pigment based on the selected reference point        cloud.

X. A database for characterizing effect pigment spots in images oflayers, the database comprising:

images of a layer comprising effect pigments, said images comprising aplurality of effect pigment spots formed within one or more images, eacheffect pigment spot characterized by at least a first and a secondlinearly projected chromaticity coordinates (u′, v′) and at least onelightness-related value L*;

one or more point clouds of effect pigment spots, each point in thepoint clouds being characterized by device-independent color coordinates(u′, v′, L);

a distance of one or more point clouds to one or more reference pointclouds which are for example each associated with a (i.e. each one, forexample each exactly one) reference effect pigment; and

a selection of a reference point cloud with shortest distance, and forexample a characterization of the effect pigment spot as the referenceeffect pigment based on the selection of the reference point cloud.

Y. A computer-based layer analysis system for characterizing effectpigment spots in images of layers, comprising:

one or more digital cameras;

one or more illumination sources;

one or more electronic displays;

one or more central processing units;

one or more non-volatile semiconductor memories comprising a storedplurality of reference point clouds which are for example eachassociated with a (i.e. each one, for example each exactly one)reference effect pigment;

one or more non-volatile semiconductor memories storing a programcomprising instructions for execution by the central processing unitfor:

acquiring color image data from the digital cameras:

forming one or more point clouds of effect pigment spots, each point inthe point clouds being characterized by at least threedevice-independent color coordinates of an effect pigment spot:

-   -   a first and a second linearly projected chromaticity        coordinates, and    -   a lightness-related value;        computing the distance of one or more point clouds to one or        more reference point clouds; and        selecting the reference point cloud with shortest distance, and        for example characterizing the effect pigment spot as the        reference effect pigment based on the selected reference point        cloud.

A second aspect of the present disclosure is described as follows:

In a first example of the second aspect, the present disclosure relatesto a computer-implemented method for identifying an effect pigment. Themethod comprises executing, on at least one processor of at least onecomputer, the following steps.

In a (for example, first) exemplary step, sample image data is acquiredwhich describes (for example, defines or represents) a digital image ofa layer comprising a sample effect pigment. For example, the sampleimage data describes a shade (i.e. a combination of the color and theeffect) of the layer. In one example, the sample image data describes adigital image (e.g. the appearance) of the layer comprising the sampleeffect pigment from a plurality of viewing directions and/or under aplurality of illumination directions.

In a (for example, second) exemplary step, sparkle point data (alsocalled sample sparkle point data or sparkle point distribution data orsample sparkle point distribution data or sample distribution data) isdetermined. The sparkle point data is determined for example based onthe sample image data and describes (for example, defines or represents)a sample distribution of sparkle points defined by the digital image,for example sample sparkle points representing the sparkle generated bythe sample effect pigment (for example, when it is illuminated). Eachsparkle point has coordinates and a value associated with each entry ofthe tupel of coordinates. For example, the sample distribution isdefined in an N-dimensional color space, wherein N is an integer valueequal to or larger than 3. For example, N=3 and for example theN-dimensional coordinate color space has orthogonal coordinate axes. Thesample distribution is generally embodied by a point cloud of measured(e.g. photographed) and then calculated data points representing thesparkle of the sample effect pigment. Where in the framework of thisdisclosure the term of color space is used to define the coordinates ofsparkle points, the term of color space is understood to define acoordinate system in which the sparkle points are defined. Such acoordinate system may be defined by more than three dimensions (i.e.more than three coordinates) if e.g. further quantities influencing thesparkle measurement are used to define the sparkle points. Such furtherquantities include but are not limited to the graininess of flakeseffecting the sparkle and contained in the effect pigment, illuminationangle, or viewing angle (the angles being defined for example relativeto a measurement surface of the layer comprising the sample effectpigment). Thus, the dimensions (coordinates) are not limited toquantities defining a color or color space within its conventionalsense.

In a (for example, third) exemplary step, sparkle point transformationdata (also called sample sparkle point transformation data) isdetermined. The sparkle point transformation data is determined forexample based on the sparkle point data and describes (for example,defines or represents) a transformation of the sample distribution intoan (N−1)-dimensional color space. The basis of the (N−1)-dimensionalcolor space is for example a proper (i.e. strict) subset of the basis ofthe N-dimensional color space. The sparkle point transformation data isdetermined for example by projecting the sparkle points from theN-dimensional color space into the (N−1)-dimensional color space. Inother words, this example encompasses projecting the sparkle pointsdescribed by the sparkle point data into a hyperplane of the space (i.e.a coordinate system such as a color space) in which the sparkle pointsare described by the sparkle point data. For example, the(N−1)-dimensional color space is defined as a two-dimensional plane(defined for example by u′ and v′ as coordinates; in one specificexample, the plane may additionally be defined by a constant value of L*or L−<L*>, such as a value of 0 [zero]) and the projection has adirection perpendicular to the plane (e.g. along or at least parallel tothe L*-axis). For example and in the case of N=3, the plane is parallelto two axes (e.g. the u′-axis and the v′-axis) of the three-dimensionalcolor space which defines the two coordinates (i.e. the basis) of thetwo-dimensional color space and for example perpendicular to the thirdaxis (e.g. the L*-axis or L*−<L*>-axis) of the three-dimensional colorspace defining (together with the aforementioned two axes) thecoordinates (i.e. the basis) of the three-dimensional color space. Thus,the sparkle points described by the sparkle point data are transformed(specifically, projected) from a for example three-dimensional referencesystem defined by u′ and v′ (the two chromaticity coordinates) and(exactly) one of L* (lightness) or L*−<L*>(deviation from meanlightness) into a two-dimensional reference system defined by u′ and v′and in one example a constant value (such as zero) of (exactly) one ofL* and L*−<L*>. The two-dimensional reference system is also calledchromaticity plane.

In a (for example, fourth) exemplary step, sparkle point distributiongeometry data (also called sample sparkle distribution geometry data) isdetermined based on the sparkle point transformation data, for exampleby applying a marginal distribution/density function to the sparklepoint transformation data. The sparkle point distribution geometry datadescribes (for example, defines or represents) a geometry, for exampleat least one of a shape or a hull (i.e. an envelope, e.g. a contour intwo, three or more dimensions), or a center and semi-axes (e.g. a radiusin case of a circular contour or spherical hull) of the sampledistribution. The geometry of the sample distribution for example has acharacteristic shape which may be defined or at least approximated as abasic geometric shape (such as a square or a box, or an ellipse or anellipsoid) which may for example be defined by a characteristic quantity(also called shape parameters, such as a center and a side length, or acenter and lengths of the semi-axes). In one example, the projection maybe performed by setting the L*- or L*−<L*>-coordinate entry to zero, andkeeping the values of the u′- and v′-coordinate entries as before foreach sparkle point.

In a (for example, fifth) exemplary step, reference distributiongeometry data (also called reference sparkle distribution geometry data)is acquired which describes (for example, defines or represents) ageometry of a reference distribution of sparkle points in the(N−1)-dimensional color space (i.e. the coordinates of the sparklepoints making up the reference distribution are defined in the(N−1)-dimensional color space). The reference distribution geometry datais predetermined (i.e. at least one of known or fixed) and is generatedfor example before execution of the method according to the firstexample of the second aspect is executed, for example by way ofcomparative measurements and/or applying other analysis techniques to atleast one sample (for example, a plurality of samples). For example, thereference distribution is generated by photographing a reference effectpigment (i.e. generating the sample image data for the referencepigment) having a known identity (for example, at least one of the typeor the composition), generating sparkle point data and sparkle pointtransformation data as well as the sparkle point distribution geometrydata for the reference effect pigment as described above for the sampleeffect pigment, and storing the resulting geometry of the sampledistribution as the geometry of the reference distribution. Thereference distribution is generally embodied by a reference point cloudstored in a non-transitory computer-readable storage medium, for examplein a digital database. The reference distribution geometry datadescribes (for example, defines or represents) a geometry, for exampleat least one of a shape or a hull (i.e. an envelope, e.g. a contour intwo, three or more dimensions), or a center and semi-axes (e.g. a radiusin case of a circular contour or spherical hull) of the referencedistribution. The geometry of the reference distribution for example hasa characteristic shape which may be defined or at least approximated asa basic geometric shape (such as a square or a box, or an ellipse or anellipsoid) which may for example be defined by a characteristic quantity(also called shape parameters, such as a center and a side length, or acenter and lengths of the semi-axes). The characteristic shape of thereference distribution and the sample distribution are for example atleast substantially the same. In one example of this step (for example,in the case of N=3), the geometry of the sample distribution iselliptical and the geometry of the reference distribution is elliptical.In another example (for example, in the case of N=4), the geometry ofthe sample distribution is ellipsoidal and the geometry of the referencedistribution is ellipsoidal.

For example, the sparkle points of the sample distribution and thereference distribution are defined in a color space (and specifically inthe same color space) defined by a (for example, one or exactly one)coordinate defining lightness L*, for example a difference L*−<L*>between a lightness of the respective sparkle points and a meanlightness <L*> of the sparkle points, and two coordinates definingchromaticity u′, v′ of the sparkle points.

In a (for example, sixth) exemplary step, reference distributionassociation data (also called reference sparkle distribution associationdata) is acquired which describes (for example, defines or represents)an association between the reference distribution (for example, thereference distribution with which the geometry described by thereference distribution geometry data is associated and therefore thegeometry of the reference distribution) and an identifier of thereference distribution. The identifier is for example unique for thereference distribution, i.e. no other reference distribution has (withinthe framework of this disclosure) the same identifier. The identifierfor defines the identity (for example, at least one of the type or thecomposition) of a reference effect pigment for which (i.e. by measuringwhich) the reference distribution has been generated. The identifier mayhave at least one of a human-readable or a machine-readable format. Ifit has a human-readable format, a string representing the identifier maybe output on a display device for a user to visually perceive theinformation about the reference effect pigment. Thus, the referencedistribution association data defines an association between thereference distribution and the reference pigment.

In a (for example, seventh) exemplary step, sample pigment identity datais determined based on the sparkle point distribution geometry data andthe reference distribution geometry data and the reference distributionassociation data. The sample pigment identity data describes (forexample, defines or represents) an identity of the sample effect pigmentfor example, in at least one of a human-readable or a machine-readableformat. If it describes the identity of the sample effect pigment in ahuman-readable format, a string representing the identity (for example,the information about the identity) of the sample effect pigment may beoutput on a display device for a user to visually perceive theinformation about the reference effect pigment. The identity of thesample effect pigment is for example determined by comparing the sparkledistribution geometry data to the reference distribution geometry datato find a reference distribution which has a geometry which is similarto the geometry of the sample distribution, for example at leastsubstantially similar at least within a predetermined (i.e. at least oneof known or fixed) limit. The identity of the sample pigment is thendetermined to be the identity of the reference pigment associated withthe reference distribution which is similar e.g. as per theaforementioned definition of similarity to the sample distribution.

For example, the reference distribution geometry data is acquired basedon comparing the geometry of a plurality of reference distributions ofsparkle points to the geometry of the sample distribution of sparklepoints. In case more than one geometry of a reference distribution out aplurality of geometries of reference distributions is compared to thegeometry of the sample distribution (and for example, if more than oneof the geometries of reference distributions is similar to the geometryof the sample distribution e.g. within the predetermined limit), theidentity of the reference effect pigment associated with the geometry ofthe reference distribution being closest (i.e. most similar orbest-fitting) to the geometry of the sample distribution is determinedto be the identity of the sample effect pigment. For example, thereference distribution geometry data is acquired based on determining,from a plurality of reference distributions of sparkle points, thereference distribution of sparkle points which best fits the sampledistribution of sparkle points. For example, the best-fitting referencedistribution of sparkle points is determined by optimizing a meritfunction which is for example defined by a weighted sum of shapeparameters. Specifically, the best-fitting reference distribution ofsparkle points is a reference distribution among a plurality ofreference distributions for which a merit function defined by adifference between the values of shape parameters characterizing thegeometry of the reference distribution on the one hand and the values ofcorresponding shape parameters characterizing the sample distribution onthe other hand is optimal, for example minimal. The best-fittinggeometry of a reference distribution therefore for example is thegeometry of a reference distribution for which an optimization algorithmsuch as a distance optimization (minimization) algorithm outputs anoptimal (i.e. best-fitting), for example minimal, result (out of aplurality of results for plural reference distributions) relative to thegeometry of the sample distribution (the geometry of sample distributionbeing an optimization target).

For example, the geometry of the reference distribution and the geometryof the sample distribution are elliptical (e.g. circular) or ellipsoidal(e.g. spherical) and the merit function describes at least one of adistance between the centers of the ellipses or ellipsoids describingthe geometry of the reference distribution and the geometry of thesample distribution, a distance between the areas of the ellipses orellipsoids describing the geometry of the reference distribution and thegeometry of the sample distribution, or a distance between theorientation angles of the ellipses or ellipsoids describing the geometryof the reference distribution and the geometry of the sampledistribution. The respective distance then represents (specifically, is)the optimization criterion to be optimized (minimized) by theoptimization algorithm used for comparing the at least one geometry of areference distribution to the geometry of the sample distribution.

In a second example of the second aspect, the present disclosure relatesto a computer-implemented method of selecting an effect pigment,comprising execution of the method according to the first example of thesecond aspect, wherein

-   -   the sample pigment identity data is determined for at least two        different reference distributions, and the sample identity data        describes at least two possible identities of the sample effect        pigment, and    -   the method further comprises executing, by the at least one        processor, steps of:    -   issuing, to an electronic display device, display data        describing (for example, defining or representing) a list        defining a prioritization (such as a goodness of fit between the        respectively associated geometry of the reference distribution        to the geometry of the sample distribution, or a measure related        to that goodness of fit) of the at least two possible        identities;    -   receiving (for example, by manual input of a user) input data        for selecting, based on the displayed list, one of the possible        identities.

The method according to the second example of the first aspectcorresponds to use of the method according to the first example of thesecond aspect for manual selection of a best-fitting reference pointcloud from a list of results comprising identities of reference effectpigments being candidates for the identity of the sample effect pigment.The list may be sorted by determined probability for each of theidentities of reference effect pigments being the identity of the sampleeffect pigment, for example based on the result of the optimizationalgorithm applied to the respectively associated geometry of thereference distribution.

In a third example of the second aspect, the present disclosure relatesto a computer-implemented method for identifying an effect pigment, themethod comprising executing, on at least one processor of at least onecomputer, steps of:

a) acquiring sample image data describing a digital image of a layercomprising a sample effect pigment (as described above for the methodaccording to the first example of the second aspect);

b) determining, based on the sample image data, sparkle point datadescribing a sample distribution of sparkle points defined by thedigital image (as described above for the method according to the firstexample of the second aspect);

c) inputting the sparkle point data into an artificial neural networkwhich has been trained on

-   -   reference distribution data describing a reference distribution        of sparkle points in an N-dimensional color space, wherein N is        an integer value equal to or greater than 3; and    -   reference distribution association data describing an        association between the reference distribution and a reference        effect pigment;

d) determining, based on the sparkle point data and the referencedistribution data and the reference distribution association data and asan output of the artificial neural network, sample pigment identity datadescribing an identity of the sample effect pigment (the sample pigmentidentity data is described above for the method according to the firstexample of the second aspect).

In a fourth example of the second aspect, the present disclosure relatesto a computer-implemented method of determining (e.g. at least one ofpredicting or correcting) a recipe (e.g. a formulation, specificallylist of ingredients such as types of flakes) for an effect pigment,comprising execution of the method according to any one of the first orsecond examples of the second aspect. In a more specific example, themethod according to the fourth example of the second aspect comprisesexecuting the method according to the second example of the secondaspect and executing, by the at least one processor, a step ofdetermining the recipe on the basis of the selected identity.

In a fifth example of the second aspect, the present disclosure relatesto a computer program which, when running on at least one processor ofat least one computer or when loaded into the memory of at least onecomputer, causes the at least one computer to perform the methodaccording to any one of the first to third examples of the secondaspect.

In a sixth example of the second aspect, the present disclosure relatesto a database comprising

-   -   reference distribution geometry data describing a geometry of a        reference distribution of sparkle points in an N-dimensional        color space, wherein N is an integer value equal to or greater        than 3; and    -   reference distribution association data describing an        association between the reference distribution and a reference        effect pigment.

In a seventh example of the second aspect, the present disclosurerelates to a non-transitory computer-readable program storage medium onwhich at least one of the program according to the fifth example of thesecond aspect or the database according to the sixth example of thesecond aspect is stored.

In an eighth example of the second aspect, the present disclosurerelates to at least one computer, comprising at least one processor anda memory, wherein the program according to the fifth example of thesecond aspect is running on the at least one processor or is loaded intothe memory, or wherein the at least one computer comprises the programstorage medium according to seventh example of the second aspect.

In a ninth example of the second aspect, the present disclosure relatesto a system for identifying an effect pigment, the system comprising:

a) the at least one computer according to the eighth example of thesecond aspect;

b) at least one electronic data storage device storing at least thereference distribution geometry data and the reference distributionassociation data; and

c) at least one digital imaging device (e.g. a digital camera having acolor sensitivity defined for example in the RGB color space) forgenerating the sample image data,

wherein the at least one computer is operably coupled to the at leastone electronic data storage device for acquiring, from the at least onedata storage device, at least one of the reference distribution data orthe reference distribution association data, and to the digital imagingdevice for acquiring, from the digital imaging device, the sample imagedata.

In higher specificity, the system according to the ninth example of thesecond aspect further comprises at least one illumination source forilluminating the layer, the layer comprising for example a corporealsurface on or in which the sample effect pigment is applied.

The illumination source is for example configured for directional(non-diffuse) illumination of the layer and a plurality of (directional)viewing geometries. At least one viewing geometry is used having aviewing angle close to the specular angle. Furthermore, a Kohinoorappearance instrument used by the inventors when making the inventionprovides assessment geometries with positive and negative aspecularangles which can be exploited to discriminate special types ofhigh-performance interference pigments.

Definitions

The disclosed method is for example a computer implemented method. Forexample, all the steps or merely some of the steps (i.e. less than thetotal number of steps) of the method in accordance with the inventioncan be executed by a computer (for example, at least one computer). Anembodiment of the computer-implemented method is a use of the computerfor performing a data processing method. An embodiment of thecomputer-implemented method is a method concerning the operation of thecomputer such that the computer is operated to perform one, more or allsteps of the method.

The computer for example comprises at least one processor and forexample at least one memory in order to (technically) process the data,for example electronically and/or optically. The processor being forexample made of a substance or composition which is a semiconductor, forexample at least partly n- and/or p-doped semiconductor, for example atleast one of II-, III-, IV-, V-, VI-semiconductor material, for example(doped) silicon and/or gallium arsenide. The calculating steps describedare for example performed by a computer. Determining steps orcalculating steps are for example steps of determining data within theframework of the technical method, for example within the framework of aprogram. A computer is for example any kind of data processing device,for example electronic data processing device. A computer can be adevice which is generally thought of as such, for example desktop PCs,notebooks, netbooks, etc., but can also be any programmable apparatus,such as for example a mobile phone or an embedded processor. A computercan for example comprise a system (network) of “sub-computers”, whereineach sub-computer represents a computer in its own right. The term“computer” includes a cloud computer, for example a cloud server. Theterm “cloud computer” includes a cloud computer system which for examplecomprises a system of at least one cloud computer and for example aplurality of operatively interconnected cloud computers such as a serverfarm. Such a cloud computer is preferably connected to a wide areanetwork such as the world wide web (WWW and located in a so-called cloudof computers which are all connected to the world wide web. Such aninfrastructure is used for “cloud computing”, which describescomputation, software, data access and storage services which do notrequire the end user to know the physical location and/or configurationof the computer delivering a specific service. For example, the term“cloud” is used in this respect as a metaphor for the Internet (worldwide web). For example, the cloud provides computing infrastructure as aservice (IaaS). The cloud computer can function as a virtual host for anoperating system and/or data processing application which is used toexecute the method of the invention. The cloud computer is for examplean elastic compute cloud (EC2) as provided by Amazon Web Services™. Acomputer for example comprises interfaces in order to receive or outputdata and/or perform an analogue-to-digital conversion. The data are forexample data which represent physical properties and/or which aregenerated from technical signals. The technical signals are for examplegenerated by means of (technical) detection devices (such as for exampledevices for detecting marker devices) and/or (technical) analyticaldevices (such as for example devices for performing (medical) imagingmethods), wherein the technical signals are for example electrical oroptical signals. The technical signals for example represent the datareceived or outputted by the computer. The computer is preferablyoperatively coupled to a display device which allows informationoutputted by the computer to be displayed, for example to a user. Oneexample of a display device is a standard computer monitor comprisingfor example a liquid crystal display operatively coupled to the computerfor receiving display control data from the computer for generatingsignals used to display image information content on the display device.The monitor may also be the monitor of a portable, for example handheld,device such as a smart phone or personal digital assistant or digitalmedia player or a (e.g. portable) spectroscopic measurement device(which also includes the computer).

The expression “acquiring data” for example encompasses (within theframework of a computer implemented method) the scenario in which thedata are determined by the computer implemented method or program.Determining data for example encompasses measuring physical quantitiesand transforming the measured values into data, for example digitaldata, and/or computing (and e.g. outputting) the data by means of acomputer and for example within the framework of the method inaccordance with the invention. The meaning of “acquiring data” also forexample encompasses the scenario in which the data are received orretrieved by (e.g. input to) the computer implemented method or program,for example from another program, a previous method step or a datastorage medium, for example for further processing by the computerimplemented method or program. Generation of the data to be acquired maybut need not be part of the method in accordance with the invention. Theexpression “acquiring data” can therefore also for example mean waitingto receive data and/or receiving the data. The received data can forexample be inputted via an interface. The expression “acquiring data”can also mean that the computer implemented method or program performssteps in order to (actively) receive or retrieve the data from a datasource, for instance a data storage medium (such as for example a ROM,RAM, database, hard drive, etc.), or via the interface (for instance,from another computer or a network). The data acquired by the disclosedmethod or device, respectively, may be acquired from a database locatedin a data storage device which is operably to a computer for datatransfer between the database and the computer, for example from thedatabase to the computer. The computer acquires the data for use as aninput for steps of determining data. The determined data can be outputagain to the same or another database to be stored for later use. Thedatabase or database used for implementing the disclosed method can belocated on network data storage device or a network server (for example,a cloud data storage device or a cloud server) or a local data storagedevice (such as a mass storage device operably connected to at least onecomputer executing the disclosed method). The data can be made “readyfor use” by performing an additional step before the acquiring step. Inaccordance with this additional step, the data are generated in order tobe acquired. The data are for example detected or captured (for exampleby a measurement device). Alternatively or additionally, the data areinput in accordance with the additional step, for instance viainterfaces. The data generated can for example be inputted (for instanceinto the computer). In accordance with the additional step (whichprecedes the acquiring step), the data can also be provided byperforming the additional step of storing the data in a data storagemedium (such as for example a ROM, RAM, CD and/or hard drive), such thatthey are ready for use within the framework of the method or program inaccordance with the invention. The step of “acquiring data” cantherefore also involve commanding a device to obtain and/or provide thedata to be acquired. In order to distinguish the different data used bythe present method, the data are denoted (i.e. referred to) as “XY data”and the like and are defined in terms of the information which theydescribe, which is then preferably referred to as “XY information” andthe like.

BRIEF DESCRIPTION OF DRAWINGS

So that the manner in which the above recited features of the presentdisclosure can be understood in detail, a more particular description ofthe disclosure, briefly summarized above, may be had by reference toembodiments, some of which are illustrated in the appended drawings. Itis to be noted, however, that the appended drawings illustrate onlyexemplary embodiments and are therefore not to be considered limiting ofits scope, for the invention may admit to other equally effectiveembodiments. Also, the reference numerals denote the same featuresthroughout the figures.

FIGS. 1 and 2 are representations combining a three-dimensional colorspace representation of a point cloud of effect pigment spots and a raydiagram.

FIG. 3 is a display combining a chromaticity diagram, a ray diagram, andnumerical data.

FIG. 4 is a perspective view of an apparatus for pigment identificationand a block diagram for a pigment identification database.

FIGS. 5A-5C are representations combining a three-dimensional colorspace representation of a point cloud of effect pigment spots and a raydiagram for a plurality of measurements of a layer comprising effectpigments.

FIG. 6 is a flowchart for a method for effect pigment identification.

FIG. 7 is a block diagram for a system for effect pigmentidentification.

FIG. 8 is a flowchart for a computer-based method for effect pigmentidentification.

FIGS. 9A1-9D3 is a table of images presenting 4 steps in the analysis ofa layer comprising effect pigments, said layer being imaged under 3illumination-viewing angle combinations.

FIGS. 10-1L-10-2B is a table of goniometric centroid plots for a firstsample and reference layers and a second sample and reference layerscomprising effect pigments.

FIG. 11 is a flow chart for a multi-goniometric geometry method for theidentification of effect pigments using centroid information.

FIG. 12 is a flowchart for a computer-based multi-goniometric geometrymethod for effect pigment identification using centroid information.

FIG. 13 presents how the (u′, v′) plane is rasterized.

FIG. 14 illustrates how each cell of the mesh feeds to an input neuronof the neural network's input layer.

FIG. 15 constitutes a flow diagram of an embodiment of the methodaccording to the first example of the second aspect.

DESCRIPTION OF EMBODIMENTS

Embodiments of the present disclosure provide an apparatus and methodfor measuring, classifying, identifying, and comparing effect pigmentscomprised in layers. A layer may be a material having a surface fromwhich effect pigments are visible to the eye or to an optical measuringdevice, for example a painted coating or a polymer sheet. The disclosurealso describes an embodiment of an apparatus comprising a color imagingsensor and a processor implementing the method. The disclosure alsoprovides a method and an apparatus to form data for forming an effectpigment reference database.

FIG. 1 is an embodiment of representation combining a three-dimensionalcolor space representation 100 of one or more point clouds of effectpigment spots and a ray diagram 200. The three-dimensional color spacerepresentation 100, abbreviated 3DCSR, comprises a first coordinate axis101 and a second coordinate axis 102 representative of chromaticitycoordinates, for example a first chromaticity coordinate, u′, and asecond chromaticity coordinate, V, for example derived from the CIE 1976(L*, u*, v*) color space also known as CIELUV. Other chromaticitycoordinates may be used such as x, y (for example of the CIE 1931 colorspace chromaticity diagram). The coordinates may be derived from othercolor space systems such as CIE 1976 UCS, CIEUVW, CIELAB, CIELCH, colorspaces of the RGB family, color spaces of the YUV family, or other colorspaces. The third coordinate axis 103 represents a value derived fromthe lightness of effect pigment spots within the point cloud of effectpigment spots 140. The value is derived to reduce the duration or theamount of computation needed for comparison between point clouds ofeffect pigment spots and may for example be a normalized value oflightness, for example a deviation from mean lightness: L*−

L*

.

Computing deviation from mean lightness (abbreviated DML) may thereforereduce the duration or the amount of computation needed for effectpigment identification, for example upon comparison with effect pigmentdata comprised in a reference database 490 (show in FIG. 4 ).

The 3DCSR 100 may further comprise a chromaticity diagram 110, forexample a (u′, v′) chromaticity diagram. The chromaticity diagram 110may comprise wavelength markings, for example along its periphery. Oneor more chromaticity diagrams 110 may be drawn on a (u′, v′) planeorthogonal at one or more coordinates of the third coordinate axis 103.The chromaticity diagram may comprise an imprint 120 corresponding to aprojection of the point cloud of effect pigment spots 140 onto thechromaticity diagram 110. The imprint 120 may be a processed renderingof the projection, obtained for example by applying a smoothing orregion growing method. The processed rendering of the projection may forexample provide a closed envelope of the projection. The processedrendering of the projection may for example provide a smoothed envelopeof the projection. The chromaticity diagram may comprise an achromaticaxis marker 130, for example a colored spot, a cone, or a line parallelto the third coordinate axis, to enable its visualization. The 3DCSR 100may comprise a fourth coordinate axis 105 that is parallel to the thirdcoordinate axis 103 to provide means for a user to obtain visualmeasurements of lightness in a coordinate space that is different fromthat of the third coordinate axis 103, for example luminance L.

The ray diagram 200 may comprise a first marker 210 for the illuminationdirection, for example represented in FIG. 1 as an arrow pointingperpendicularly towards the surface of the layer. The ray diagram maycomprise a second marker 220 for the aspecular direction, for example anaspecular viewing direction, for example represented in FIG. 1 as anarrow pointing towards the surface of the layer, for example at an angleof 15°. The ray diagram may comprise a third marker 230 for the viewingdirection, for example represented in FIG. 1 as an arrow pointing awayfrom the surface, for example at an angle of 15°. The diagram maycomprise an aspecular angle marker 232, for example represented as anarc between the first marker 210 and the second marker 220. The raydiagram may comprise a ray diagram angles box 240, the top side 242 ofwhich may serve as a representation of the surface of the layer withinthe ray diagram 200. As an example for ray diagram 200, the ray diagramangles box 240 comprises the sequence r15as15 corresponding for thefirst part (r15) to the second marker's aspecular direction set at anangle of 15° with respect to surface normal and for the second part(as15) to the first marker's angle of 15° with respect to the secondmarker.

A graphical user interface displaying a representation of FIG. 1 , forexample implemented on a computer or on a measurement device's display,may enable a user to move the chromaticity diagram's plane in adirection parallel to that of the third coordinate axis 103, for exampleto obtain measurements of raw or processed values related to thecross-sectional area of the point cloud 140 intersecting thechromaticity diagram's plane 110.

FIG. 2 presents a second embodiment of the representation presented inFIG. 1 , here displaying a second set of effect pigment data forming afirst and a second point clouds of effect pigment spots 140-1 and 140-2,respectively, each of which is projected as a first and a secondprojection of effect pigments spots 120-1 and 120-2, respectively, ontothe chromaticity diagram 110.

FIG. 3 presents an integrated display 300, for example to be displayedon a computer or on a measurement device's display. The integrateddisplay 300 may comprise one or more statistical data areas 310, one ormore graphical data areas 115, or one or more ray diagrams 200. Thestatistical data area 310 may present information items related to theone or more point clouds 140-1, 140-2, or their projections 120-1,120-2, for example: one or more effect pigment identification names orreferences; the relative content of said effect pigment comprised withinthe layer, for example expressed as a percentage; the relative ratio ofa first effect pigment with respect to a second effect pigment, forexample expressed as a percentage; the coordinates of the centroids ofone or more point clouds; the coordinates of the centroids of a crosssection of the one or more point clouds intersected by a chromaticityplane, such as a (u′, v′) plane; the areal or volumetric density of oneor more point clouds; the average size of spots that form the one ormore point clouds; the raw or processed measured area of the projectionof the one or more point clouds onto the chromaticity diagram; the rawor processed measured area of the intersection of the one or more pointclouds onto the chromaticity diagram; the variance or other statisticalmeasure of one or more of the presented information; or the aggregateover a plurality of point clouds of one or more of the presentedinformation. The information presented may relate to a measured pointcloud 140-1, 140-2 or originate from data stored in a reference database490.

The graphical data area 115 may present a chromaticity diagram 110comprising one or more projections or cross-sections 120-1, 120-2, 120-3of measured data or stored reference data for effect pigment pointclouds. The graphical data area 115 may present respective centroids125-1, 125-2, 125-3 of projections or cross-sections 120-1, 120-2,120-3. The projections or cross-sections may be displayed as having oneor more processed contours that are smoother than a raw projection orcross-section of the point clouds. The processed contours may forexample be obtained by transforming the raw projections orcross-sections using two-dimensional or three-dimensional methods suchas: smoothing filters, for example Gaussian-based or kernel-basedfilters; or image segmentation methods, for example simulated annealingmethods or watershed methods. The graphical data area 115 mayadvantageously reduce the time needed by an operator to verify thatresults provided by an automated effect pigment analysis system 700(presented in FIG. 7 ) are correct. The graphical data area 115 mayadvantageously enable an operator to make direct measurements usingdisplayed information. For example, an operator may compare graphicallyor numerically the positions, for example based on displayedcoordinates, of measured centroids 125-1, 125-2 to those of referenceeffect pigments data stored in a reference database 490.

FIG. 4 presents a perspective view of a handheld apparatus 400 formeasuring, classifying, identifying, and comparing effect pigmentscomprised in surface layers. The handheld apparatus 400 may comprise ahousing 410, for example designed to be ergonomically shaped and ahandle portion 420 that may comprise one or more human interface sensorssuch as touch sensors, pressure sensors, or switches enablinginteraction with the measurement process or information displays. Theapparatus 400 may comprise one or more display screens 430, for examplea touch-sensitive screen that may enable an operator to interact withthe measurement process or the information displayed. The apparatus 400may comprise one or more human interface sensors 440, for example atactile sensor, for example a 5-way switch. The apparatus 400 maycomprise an imaging and illuminations subsystem 450 (not shown) that isentirely or partially enclosed within the housing 410. The apparatus 400may comprise one or more processing units 720 and one or more storageunits 730 (FIG. 7 ).

The handheld apparatus 400 may operate in relationship with a referencedatabase 490. The reference database 490 may be stored withinnon-volatile memory of the handheld apparatus, for example comprised instorage unit 730. The database 490 may be stored in the storage unit730, for example in non-volatile memory, of a distant computer systemand be accessible via wired or wireless communication. The database maycomprise appearance parameters 495 related to reference effect pigmentspot clouds, for example one or more items of information displayable inthe statistical data area 310, the graphical data area 115, or the raydiagram 200. The appearance parameters my further comprisetexture-related parameters, sparkle grade-related parameters, sparklespot-related parameters, angle-dependent parameters, or depth-relatedparameters. The database may comprise reference images 497, for examplepoint cloud images or images of layers, for example images of layersshown in FIG. 9A1 to 9D3. The data, for example images or appearanceparameters, acquired or formed by the handheld apparatus 400 may bestored within the reference database 490. The reference database 490 maytherefore be read from or written to by the handheld apparatus.Furthermore, images or appearance parameters, acquired or formed by thehandheld apparatus 400 may be used to update data stored in the database490.

FIGS. 5A, 5B, 5C present three 3DCSR and ray diagram representations fordata acquired from a layer comprising effect pigments. Each of therepresentations display data acquired under different illumination andviewing angle combinations, for example with respect to a normal fromthe surface of a coated sample comprising effect pigments. FIG. 5A is areproduction of FIG. 2 where variable data elements acquired atillumination and acquisition setting r15as15, namely 210, 220, 230, 232,120-1, 120-2, 140-1, and 140-2 have been renamed 210A, 220A, 230A, 232A,120-1A, 120-2A, 140-1A, and 140-2A, respectively. FIG. 5B presents datafor the same layer as that presented in FIG. 5A, the difference with thedata in FIG. 5A is that data is here acquired at setting r15as45,thereby resulting in respective elements 2100, 220B, 230B, 232B, 120-1B,120-2B, 140-1B, and 140-2B. FIG. 5C presents yet more data for the samelayer as that presented in FIG. 5A, here acquired at setting r15as45,thereby resulting in respective elements 210C, 220C, 230C, 232C, 120-2C,and 140-2C.

An apparatus for measuring, classifying, identifying, and comparingeffect pigments comprised in surface layers may therefore present one ormore representations presenting data acquired at one or more settings asthose presented in FIGS. 5A, 5B, 5C for a given layer comprising effectpigments. The apparatus, for example a computer comprising a display ora handheld apparatus 400 may present the representations of FIGS. 5A,5B, 5C in a sequence, for example at a speed providing the impression ofan animated sequence. In a corresponding way, the integrated display 300may display a sequence or animated sequence of measurements in the formof data, statistical results, and graphical results as described forFIG. 3 . The ability to quickly navigate through a set of measurementsacquired under a plurality of geometry settings may advantageouslyprovide to a user improved means to identify effect pigments in layers.Furthermore, if a large enough set of data are available, for examplespot clouds, acquired under a plurality of geometry settings, a smoothlyanimated sequence may be presented to an operator via datainterpolation, for example by tracking individual spots acquired betweena first geometry setting and a second geometry setting, or for exampleby using graphical morphing methods. An automated or semi-automatedeffect pigment identification method may benefit from the addeddimension provided by data acquisition under a plurality of geometrysettings, as highlighted in FIGS. 10-1L to 10-2B.

FIG. 6 presents steps of a method 600 t for identifying effect pigmentscomprised in a layer 600. The steps of the method may be stored withinnon-volatile memory storage of a computer. The method comprises a stepof providing images 610 wherein one or more images of layers areprovided. For example, the images may be acquired by the handheldapparatus 400, by another apparatus (not shown) for the acquisition ofimages of layers, or be downloaded from a database, An apparatus for theacquisition of images of layers is preferably an imaging system capableof obtaining images of a layer under a plurality of imaging andillumination angles, for example angles measured with respect to anormal to the surface of the layer. For example, the images may beobtained using a goniometric photographic apparatus. Another apparatusfor acquiring the images may comprise a plurality of photographiccameras arranged to view the layer under a plurality of angles. Theimages may be obtained as single shot images or may be synthetic imagesformed by combining images acquired under a plurality of exposures, suchas so-called high dynamic range (HDR) images. Exemplary embodiments ofsuch images are presented in FIGS. 9A1, 9A2, 9A3. The images may beobtained directly using a so-called high dynamic range imaging sensor.The images may be of a portion of a layer or the totality of a layer.The images preferably cover an identical portion of the layer or overlapeach other to an extent greater than 30%, such as greater than 50%, suchas greater than 80%. The images may be stitched together to form astitched image covering a larger viewfield.

Expressed programmatically, step 610 may correspond to: for a range ofillumination angles with respect to the layer's surface; for a range ofviewing angles with respect to the sample's surface: acquire one or morecolor images of a portion of the sample surface. The color image may beobtained using one or more color imaging sensors or may be formed byusing a plurality of images acquires using one or more monochromaticimaging sensors imaging the layer in turn through a plurality offilters, such a red, green, and blue filters. The imaging sensors maycomprise a linear array of photoreceptors, such as a one-dimensionalarray, or a two-dimensional array of photoreceptors. The images may beacquired as part of an imaging sequence, for example a movie. Theimaging sequence may be acquired under constant or changing conditionssuch as changes in: lighting conditions; lighting wavelength; lightingpolarization; spatial lighting conditions; camera position; cameraangle; camera distance from layer; camera lens magnification; cameralens focus; or light filtering between layer and camera sensor, such asfilter color or filter polarization. In the following, an image may bean image from a sequence of images or an image aggregated from aplurality of images. All or part of the images may be subject of thesteps of the method.

The method 600 comprises a step 620 of computing lightness data offeatures within the image. For example, for the application ofidentifying effect pigments in a layer, the step 620 may comprisecomputing the lightness of pixels within effect pigment spots in theimage. As a further example, step 620 may comprise computing the averagelightness of pixels within effect pigment spots in the image. In thisstep, the image may be converted, from for example red-green-blue (RGB)image values, into standardized and calibrated device-independent XYZtristimulus values. An estimate of the average image color may becomputed. Alternatively or in conjunction, a measure of the backgroundcolor may be acquired, for example using a spectrophotometer. Theaverage image color or the background color may be subtracted from theimage. The luminance channel of the image may be filtered. For examplethe filtering may be a thresholding adjusted to augment the contrast ofeffect pigment spots, thereby forming raw spots as white dots againstblack background. The raw spots are shown in FIGS. 9B1, 9B2, 9B3 asblack dots against a white background, a negative image prepared forprinting and clarity of this disclosure. The image comprising raw spotsenables the detection and location of effect pigment spots, for exampleby detecting spots in the image using their grey level value above athreshold. Described programmatically, computing lightness datacomprises: for each (illumination angle, viewing angle) pair of whichimages the lightness is to be computed; for each pixel within a rawspot: compute coordinates (u′, v′) such that:

${u^{\prime} = \frac{4X}{X + {15} + {3Z}}},{{v^{\prime} = \frac{9Y}{X + {15} + {3Z}}};}$compute the CIELUV lightness L* where: with

L^(*) = 116f(Y/Y_(n)) − 16,${f\left( {Y/Y_{n}} \right)} = \left\{ {\begin{matrix}\left( {Y/Y_{n}} \right)^{1/3} & {{{if}\ \left( {Y/Y_{n}} \right)} > \left( {24/116} \right)^{3}} \\{{\left( {841/108} \right)\left( {Y/Y_{n}} \right)} + {16/116}} & {{{if}\ \left( {Y/Y_{n}} \right)} \leq \left( {24/116} \right)^{3}}\end{matrix},} \right.$and where X, Y, Z denote tristimulus values and Y % denotes a luminanceof a reference white point. The average of each of u′, v′, and L* may becomputed for each pigment spot and used in subsequent computations, eachas a representative value of a given spot. A further value that may becomputed is

L

, the mean of lightness L* of all effect pigment spots. Yet a furthervalue that may be computed is L*−

L*

, the deviation from mean lightness.

The method 600 comprises a step 630 of forming one or more point cloudsof spots, for example by plotting the values of u′, v′, and L*−

L*

in a three-dimensional coordinate frame as presented in FIGS. 1, 2, and5A to 5C. Forming point clouds or other computational steps may be donefor each (illumination angle, viewing angle) pair. Furthermore, toadvantageously reduce the computational effort, forming point clouds orother computational steps may be done for each of a subset of(illumination angle, viewing angle) pairs that may be identified ascomprising most of the information relevant to the identification ofeffect pigments comprised in the layers that have been imaged.

The method 600 may comprise a step 640 of forming point cloud clusters(PCC) from the point cloud of spots formed at step 630 of forming apoint cloud of spots. Forming point cloud clusters may be using one ormore clustering methods, for example: a k-means method, for example thek-means++ method; a principal component analysis (PCA) method; a regiongrowing method, for example based on forming a grid in the (u′, v′)plane, and deriving one or more statistical values representative of theoccupancy of grid cells by spots or (u′, v′) plane-projected spots. Thegrid may for example have a 64×64 resolution to cover the surface of thechromaticity diagram. The grid may be centered onto the white point ofthe chromaticity diagram. Each clustering method may be preceded orsupplemented by an outlier detection step. Outlier detection may beperformed in, for example, a two-dimensional plane, such as the (u′, v′)plane, or a multi-dimensional space, such as the three-dimensional (u′,v′, L*−

L

) space.

The method 600 may comprise a step 650 of computing the mean lightnessof each point cloud clusters:

L*

_(PCC). The step 650 may also include computing the mean lightness of aplurality of point cloud clusters. The method 600 may comprise a step660 of computing the deviation from mean lightness of each point cloudclusters: L*_(PCC)−

L*

_(PCC). The step 660 may also include computing the deviation from meanlightness of a plurality of point cloud clusters.

The method 600 may comprise a step 670 of analyzing one or more pointcloud clusters. The step 670 of analyzing one or more point cloudclusters may be accomplished by a point cloud cluster analyzer 740 (FIG.7 ), for example implemented as software, as software loadable fromnon-volatile memory, or as a dedicated electronic circuit, for exampleimplemented using field-programmable gate array (FPGA) technology or anelectronic very large scale integrated (aVLSI) circuit technology. Anautomatic pigment identification method may be used for the analysis.The automatic effect pigment identification method may for example use atwo-dimensional pattern matching method, for example by dividing the(u′, v′) plane into a plurality of cells, for example a uniform grid ofcells, and feeding a value computed for each cell, for example a valuecharacterizing the occupancy of the cell by one or more spots orprojected spots, for example in one embodiment a continuous valuerelative to the areal occupancy, or for example in another embodiment abinary value triggered by an occupancy area threshold, into anartificial neural network (ANN). In essence, the classifier computes adistance of one or more point clouds, for example formed from acquiringimages of layers, to one or more reference point clouds. Other inputs tothe classifier may include: measurement of skewness, for example basedon moments with respect to achromatic axis; or bivariate scatterellipses, for example using the coordinates of their foci. The automaticeffect pigment identification method may for example use athree-dimensional pattern matching method, for example by dividing thethree-dimensional (u′, v′, L*−

L*

) space into uniform or non-uniform voxels (for example based on a k-dtree or an octree) and, similarly to the two-dimensional method, feedinga value for each voxel into a pattern matching method, for example anANN.

The automatic effect pigment identification method may for example use afour- or five-dimensional pattern matching method, for example using afour- or five-dimensional space defined by (u′, v′, L*−

L

, ray geometry). The ray geometry characterizes the angles of theimaging apparatus and the lighting with respect to the layer beingimaged or with respect to each other, for example r15as15, r15as45,r15as80, an exemplary embodiment is presented FIGS. 10-1L to 10-2B. Thefour- or five-dimensional space may be divided into a four- orfive-dimensional voxel space, feeding a value for each voxel into apattern matching method, for example an ANN.

The classifier, for example an artificial neural network, may compareits input derived from data measured using the apparatus 400 with effectpigment data stored in reference database 490. For example measured dataand stored data may be compared in real-time by the classifier uponmeasurement using the apparatus 400. Alternatively, measured data andstored data may be compared after one or more measurements using theapparatus 400. The classifier may be executed on a processing unit 730comprised or embedded within the apparatus 400, on a processing unitexternal to the apparatus 400, or on a combination of embedded andexternal processing units. An artificial neural network classifier, maystore a portion or the entirety of the reference database 490 as weights(i.e. the weights of the artificial neural network). The weights and theconnective structure of the artificial neural network may be stored innon-volatile memory. In one embodiment, the artificial neural networkmay be implemented as a field-programmable gate array (FPGA) circuit. Inanother embodiment, the artificial neural network used for effectpigment classification may be implemented as an analog electronic verylarge scale integrated (aVLSI) circuit.

The output data 860 output by the step 670 of analyzing one or morepoint clouds may comprise: the identity 850 (FIG. 8 ) of one or moreeffect pigments corresponding to one or more point cloud clusters; avalue or a qualifier related to the level of confidence, for example theprobability, of the identity of each of to one or more point cloudclusters; a value related to (for example, defining) the grade 854 orcoarseness of each of the effect pigments identified or not identifiedin the layers; a qualifier related to (for example, defining) the gradeor coarseness of each of the effect pigments identified or notidentified in the layers, for example grade qualifiers such as ‘extrafine’, ‘fine’, ‘medium’, or ‘coarse’; an effect pigment class 852, forexample related to (for example, defining) the material or morphology ofthe pigment or pigment flakes, examples of which may be aluminiumflakes, inorganic or organic-coated flakes, interference pigmentclasses, examples of which may differ in substrate such as mica,synthetic aluminium-oxide, synthetic borosilicate, synthetic silicaflakes, aluminium, or liquid crystal polymers; and a table, for examplea spreadsheet, listing all detected effect pigment types andcorresponding output data of the step 670 of analyzing one or more pointclouds. Further output data may include statistical data related toaverage spot size, variance in spot size, or statistical dispersion, forexample expressed using a Gini coefficient. One or more data 860 outputby the goniometric pigment classifier may be stored in the referencedatabase 490.

FIG. 7 presents a block diagram for an embodiment of an effect pigmentanalysis system 700. The embodiment may be one or more handheldapparatus 400 of FIG. 4 , one or more apparatuses comprised in a system,for example a system for forming coating layers or verifying the qualityof coating layers, for example coating layers on automotive systems. Inone embodiment, the effect pigment analysis system 700 may be embeddedin a handheld apparatus that provides image acquisition and analysisfunctions. In another embodiment, the effect pigment analysis system 700may be a system comprising independent, for example part of which arecomprised in separate housings, acquisition, processing, storage,display, and analysis components. The system may comprise an effectpigment image acquisition apparatus 710. The effect pigment imageacquisition apparatus 710 may be a handheld apparatus, a digital camera,a purpose-built system comprising a plurality of imaging devicesarranged over a plurality of locations so as to view a sample under arange of viewing angles, or a system comprising at least onegoniometrically mobile imaging apparatus. The system 700 may for examplebe entirely or partly comprised in a robotic system, for example arobotic system comprising a manipulator with the effect pigment imageacquisition apparatus 710 comprised within an end effector of themanipulator. The effect pigment analysis system 700 may comprise aprocessing unit 720, a storage unit 730, a point cloud cluster analyzer740, a user interface unit 750 which may comprise a display unit 760 anda user input unit 770, and a communication unit 780.

The effect pigment analysis system, for example its processing unit 720or its storage unit 730, may include a central processing unit (CPU)(not shown), memory (not shown), and support circuits (or 1/O) (notshown). The CPU may be one of any form of computer processors that areused in low power mobile electronic devices, embedded electronicdevices, general purpose computers, or computers used in industrialsettings for the measurement of layers, coatings, or optical features ator near the surface of materials. The memory is connected to the CPU,and may be one or more of a readily available memory, such as flashmemory, random access memory (RAM), read only memory (ROM), floppy disk,hard disk, or any other form of digital storage, local or remote.Software instructions and data can be coded and stored within the memoryfor instructing the CPU. The support circuits are also connected to theCPU for supporting the processor in a conventional manner. The supportcircuits may include cache, power supplies, clock circuits, input/outputcircuitry, subsystems, and the like. A program (or computerinstructions) readable by the effect pigment analysis system determineswhich tasks or steps for identifying effect pigments are performable.Preferably, the program is software readable by the effect pigmentanalysis system 700 or one or more of its computer-based components.

In some embodiments, the point cloud cluster analyzer 740 may beimplemented as a software program loadable from non-volatile memorycomprised in the storage unit 730 into the processing unit 720. In otherembodiments, the point cloud cluster analyzer 740 may be implemented asone or more dedicated electronic devices: for example, comprisingprocessors dedicated to multi-dimensional processing such as one or moregraphical processing units; or as another example, field-programmablegate array (FPGA) technology devices; or as yet another example,electronic very large scale integrated (aVLSI) circuit technologydevices.

The user interface unit 750 may comprise a display unit 760, for examplea tactile display (e.g. a touchscreen) that may also act as part ortotality of the user input unit 770, that may enable an operator tointeract with the measurement process or the information displayed. Anoperator may for example use the user interface unit to rotate or zoominto the three-dimensional color space representation 100 or thegraphical data area 115. An operator may browse through a set ofmeasurements acquired at a range of illumination and viewing angles, thevalue of which is presented in the ray diagram angles box 240, bydragging, for example using a finger against the display or a mouse, thefirst, second, or third markers 210, 220, 230 comprised in ray diagram200. The user input unit 770 may comprise one or more buttons, tactiledisplays, or tactile sensors, examples of which have been mentioned inreference to the handheld apparatus 400.

The communication unit 780 may comprise wired or wireless communicationdevices enabling real-time transfer during measurement orpost-measurement transfer of data to and from a database, for examplereference database 490.

FIG. 8 presents a block diagram for an effect pigment classificationmethod 800. The effect pigment classification method 800 comprises aclustering method 820 and may comprise a two-dimensional orthree-dimensional input 840 feeding two- or three-dimensional spot data(for example coordinates, statistical distribution) into an effectpigment classifier 810. The effect pigment classifier 810 may forexample be implemented within the point cloud cluster analyzer 740. Theeffect pigment classifier 810 may exchange data with, for examplereceive data from, the reference database 490. The clustering methodcomprises forming: one or more cluster centroids 822 (an exemplaryembodiment of which is plotted in FIGS. 10-1L to 10-2B); one or morecluster area values 824, for example: the area of the cluster projectedonto the chromaticity diagram 110 or the surface of the cluster'senvelope; and a cluster gamut 826, for example a (u′, v′), (u*, v*), (c,h), or (L*-related, a*, b*) gamut computed for each cluster. The effectpigment classifier 810 outputs results 860, for example: pigmentidentity 850, pigment class 852, or pigment grade 854. The results 850,852, 854 may include values, for example indicating the level ofconfidence of a result. The effect pigment classifier 810 may operatewith two-dimensional data, for example two-dimensional coordinates ofthe cluster of spots 140, 140-1, 140-2 projected onto the chromaticitydiagram 110 in the u′, v′ plane (or a, b plane in a CIELAB L*a*b* colorspace), or may operate on three-dimensional data, for example in (u′,v′, L*−

L*

) space.

FIGS. 9A1 to 9D3 are images, arranged in a table, presenting 4 steps inthe analysis of a layer comprising effect pigments, said layer beingimaged under 3 illumination-viewing angle combinations. The images mayshare a common calibration basis. The images may for example have beenacquired by one or more color digital cameras. FIGS. 9A1 to 9D3 areimages of gonioapparent blue color layers comprising effect pigments.The images are acquired (photographed) at r15as15 (first column FIGS.9A1, 9B1, 9C1, 9D1), r15as45 (second column FIGS. 9A2, 9B2, 9C2, 9D2),and r15as80 (third column FIGS. 9A3, 9B3, 9C3, 9D3). The first row ofimages 9A1, 9A2, 9A3 may be acquired, for example photographed, using ascanning sensor or a two-dimensional array sensor, as single shot imagesor may be synthetic images formed by combining images acquired under aplurality of exposures, such as so-called high dynamic range (HDR)images.

The second row of images 9B1, 9B2, 9B3 is formed, for example byconverting the images to grey-scale images and filtering them, forexample by thresholding, thereby forming black images with white spots.The images are presented in this disclosure as negative images (whiteimages with black spots) for printing and clarity. The third row ofimages 9C1, 9C2, 9C3, so-called sparkle-free images, is formed bysubtracting the second row of images (the black images with white spotsversion) from the first row of images. The third row of images 9D1, 9D2,9D3, so-called sparkle pattern images, is formed by masking the firstrow of images with the second row of images (the black images with whitespots version).

FIGS. 10-1L to 10-2B each show a table of goniometric centroid plots fora first sample and reference layers and a second sample and referencelayers comprising effect pigments. FIGS. 10-1L to 10-2B show thethree-dimensional L*, a*, b* (CIELAB) coordinates of the centroid, forexample of a point cloud of effect pigment spots 140, 140-1, 140-2 or ofa point cloud cluster, of measured sparkle color for a first samplelayer (curves 1010SL, 1010SA, 1010SB) and a first reference layer(curves 1010RL, 1010RA, 1010RB) (FIGS. 10-1L to 10-1B) and a secondsample layer (curves 1020SL, 1020SA, 1020SB) and a second referencelayer (curves 1020RL, 1020RA, 1020RB) (FIGS. 10-2L to 10-2B). FIGS.10-1L, 10-1A, 10-1B are plots for a first layer embodiment over a rangeof goniometric geometries r15as-45 to r15as80. FIGS. 10-1L, 10-1A, 10-1Bare plots for a second layer embodiment over a range of goniometricgeometries r15as-45 to r15as80. Each graph plots a lightness value, forexample L* (FIGS. 10-1L and 10-2L), and color-opponent dimensions, forexample a* (FIGS. 10-1A and 10-2A), or b* (FIGS. 10-1B and 10-2B) of aspot cloud's centroid 125-1, 125-2 (shown for example in FIG. 3 ) as afunction of illumination and viewing geometry. An alternative graphicalembodiment example may use lightness L*−<L*>, and color-opponentdimensions u′, v′. In the illustrated embodiments depicting geometricalcoordinates from r15as-45 to r15as80, the plots have not been connectedbetween r15as-15 and r15as15 but further measurement would enable acontinuous plot to be drawn.

In the embodiment presented in FIGS. 10-1L to 10-1B, the first sampleand reference layers comprise grey effect color shades pigmented withthree different types of effect pigments: a first effect pigment basedon aluminum, a second effect pigment providing a green interferencecolor or a blue-green combination color, and a third effect pigmentproviding a green color. Samples and reference depicted in FIGS. 10-1Lto 10-1B are based on combinations of three different types of effectpigments, where two types are identical; only the third effect pigmenttype is different. Analysis of FIG. 10-1A shows a difference greaterthan 1.0 for centroids at r15as-15 and r15as15, thereby indicating asubstantially visible mismatch between the first sample and the firstreference. Furthermore the trends in FIG. 10-1A from r15as-45 tor15as-15 or from r15as15 to r15as45 are different between the firstsample (plot 1010SA) and the first reference (plot 1010RA), therebystrengthening the indication of a substantially visible mismatch betweenthe first sample and the first reference.

In the embodiment presented in FIGS. 10-2L to 10-2B, the second sampleand reference layers are based on effect pigments of the same types asthose presented in the embodiment of FIGS. 10-1L to 10-1B. The amountsor relative proportions of each effect pigments are, however, differentin the layers of FIGS. 10-2L to 10-2B from the layers of FIGS. 10-1L to10-1B. FIGS. 10-2L to 10-2B, show a very close match of centroids andtrends between sample layer and reference layer for the L* (FIG. 10-2L)or b* (FIG. 10-2B) plots. Although there is some noticeable discrepancyin the a* ordinates or trends between sample and reference (FIG. 10-2A)from r15as-45 to r15as-15 or from r15as15 to r15as45, the discrepancy isof a smaller amplitude (less than 1.0) for the second sample-referencepair of FIG. 10-2A than it is for the first sample-reference pairdepicted in FIG. 10-1A. There is therefore a greater similarity betweenthe second sample and the second reference than between the first sampleand the first reference.

Hence, analysis of centroid color positions of sparkle spots as afunction of measurement geometry provides valuable information about thepigmentation chosen for a color match. Measurement of the differences incentroid position or trends in the L*, a*, or b* graphs may enable aperson designing layers comprising effect pigments to adjust a layer'sdesign so as to better match a reference layer design. For example, themethod may enable a layer designer to prepare a layer that purposelyvaries from a known reference layer, for example to design a range oflayers. In another example, the method may enable a layer designer todesign a layer that better matches an existing layer, for example torepair the existing layer.

A measurement system may therefore usefully provide to a designer thecentroid-based graphs of the type presented in FIGS. 10-1L to 10-2B. Thegraphs may be displayed on a computer screen or on a display embedded ina measurement apparatus, as shown in FIG. 4 . An automated effectpigment analysis system may derive statistical information from the L*,a*, or b* coordinates as a function of geometry, as displayed in FIGS.10-1L to 10-2B, and provide analysis results and recommendations to aperson analyzing or designing layers, for example layers comprisingeffect pigments.

FIG. 11 is a flowchart for a multi-goniometric geometry method 1100 forthe identification of effect pigments using centroid information. FIG.11 presents a variation of the method presented in FIG. 6 and can beunderstood in relation to forming the data presented in FIGS. 10-1L to10-2B presenting L*, a*, b* values of point cloud centroids versusgoniometric geometry. The method iterates at step 1110 by performingcomputation steps for each goniometric geometry that have formed imagesof a layer, for example from goniometric geometries 15as-45 to 15as80,comprising: providing images 610; computing lightness data 620; formingone or more point clouds 630; forming one or more point cloud clusters(PCC) 640; and computing one or more point cloud centroids 1120.Computing one or more point cloud centroids 1120 may for example be donefor one or more goniometric geometric settings for at least one of L*,a*, or b*, or at least one of L*, u′, v′. The method 1100 may furthercomprise forming a graph or table, for example a look-up table, of pointcloud centroids versus goniometric geometry. The graph may be plotted ona display and used for analysis by an operator. The look-up table may beused as a further source of reference data by an effect pigmentclassifier. The method 1100 may yet further comprise a step 1140 ofcomparing point cloud centroids to reference data 495, 497 comprised inthe reference database 490.

FIG. 12 is a flowchart for a computer-based multi-goniometric geometrymethod 1200 for effect pigment identification using centroidinformation. FIG. 12 is similar to FIG. 8 but for example comprises aplurality of goniometric cluster data derived from goniometricmeasurements, thereby offering the possibility to reduce classificationerrors thanks to a larger dataset. The multi-goniometric geometry method1200 comprises forming a set of goniometric cluster data 1210, themethod comprising applying a clustering method 820. The clusteringmethod comprises forming: one or more cluster centroids 822 (anexemplary embodiment of which is plotted in FIGS. 10-1L to 10-2B); oneor more cluster area values 824, for example: the area of the clusterprojected onto the chromaticity diagram 110 or the surface of thecluster's envelope; and a cluster gamut 826, for example a u′, v′ orL*-related, a*, b* gamut computed for each cluster. The method maycomprise a two-dimensional or three-dimensional input 840 feeding two-or three-dimensional spot data (for example coordinates, statisticaldistribution) into a goniometric effect pigment classifier 1230. Themethod may further comprise a step 1220 supplying to the effect pigmentclassifier 1230 one or more of: the coordinates of one or more clustercentroids (for example cluster centroids 822); or statistical dataderived from the goniometric plots of centroids or look-up table, ofpoint cloud centroids versus goniometric geometry, an exemplary of whichis presented in FIGS. 10-1L to 10-2B via curves 1010SL, 1010SA, 1010SB,1020SL, 1020SA, and 1020SB. The goniometric effect pigment classifier1230 may for example be implemented within the point cloud clusteranalyzer 740. The goniometric effect pigment classifier 1230 mayexchange data with, for example receive data from, the referencedatabase 490. The goniometric effect pigment classifier 1230 outputsresults 860, for example: pigment identity 850, pigment class 852, orpigment grade 854. Output results 860 may be added to the referencedatabase 490, for example via the goniometric pigment classifier 1230.

FIG. 13 presents how the (u′, v′) plane is rasterized:

-   -   1. The (u′, v′) region enclosing the entire chromaticity diagram        110 is rasterized to form a grid or mesh. A suggested resolution        of the mesh is 66×66 but a range from 10×10 to 500×500, for        example 20×20 to 100×100, for example 50×50 to 80×80 is also        possible within the framework of this disclosure.    -   2. Then, because that rasterization would generate many empty        cells, all cells that do not include any points from any of the        reference distribution sparkle points are removed so that what        remains is a core grid or mesh.    -   3. One then adds back one or more surrounding rows of cells, for        example 10% to 20% of the span of the initial mesh, around the        core mesh so that subsequent sample sparkle points can be caught        in the mesh. There is no need to add cells that would not be        contained within the rounded triangle formed by the chromaticity        diagram 110.

FIG. 14 illustrates how each cell of the mesh feeds to an input neuronof the input layer of the neural network. Only 3 input neurons arerepresented for simplicity. The data that may be fed into each inputneuron may for example be a sparkle grade (sparkle value, where sparklegrade=average sparkle intensity x input cell area). The network (e.g., aso-called Perceptron) has one input layer, at least one hidden layer (4neurons shown), and one output layer (2 outputs shown). The hiddennetwork also has a bias (square box) that may be adjusted (e.g. valuebetween 0.0001 and 1) to scale the weights of each connection going fromthe bias to the hidden layer cells. One may also add an output networkbias (not shown). The number of output neurons should be chosen so thatthe range of reference distribution association data can be covered bythe output layer. For example if the network is trained to recognize 16references and the output layer is made of cells that can only representbinary values (0 or 1) then 4 output cells will be needed. The networkmay have 30 input cells and 30 output cells and may have a hidden layerwith about 75 cells. The appropriate dimensions of the network can onlybe correctly determined through experimentation. When the neural networkis in a mode for learning the reference sparkle points, the weightsassociated with each connection of the neural network may be updated by,for example, a backpropagation neural network learning paradigm.

FIG. 15 constitutes a flow diagram of an embodiment of the methodaccording to the first example of the second aspect. In step S1501, theappearance of a sample is measured by taking an RGB HDR image at variousdirectional geometries which represents the digital image described bythe sample image data. In subsequent step S1502, the camera image isconverted from an RGB HDR format to a device-dependent standardizedimage in XYZ color space format. This is followed by step S1503 ofsegmenting the XYZ image into sparkle and background contributions. Inthe following step S1504, the experimental sparkle color distributionfunction is derived from the sparkle pattern which corresponds todetermining the sparkle point data. In step S1505, the XYZ coordinatesof the sparkle color distribution function are converted to a desireduniform color space such as (L, u′, v′). In step S1506, the marginaldistribution function is computed in the u′-v′-plane for a given squaregrid, which is followed by step S1507 of analyzing the shape, forexample contour, of the marginal density function (e.g. by estimation ofa scatter ellipsis received from bivariate statistics) in order todetermine the sparkle point distribution geometry data. Step S1508 thenretrieves the shape parameters of reference sample from a database,which encompasses acquisition of the reference distribution geometrydata. The sample pigment identity data is then determined in step S1509be carrying out a similarity analysis of the shape parameters of thereference samples relative to the determined shape of the marginaldistribution function to identify the best-matching reference sample(i.e. reference effect pigment). This is done using a merit functionconstructed as a weighted sum of shape parameters. The best-matchingreference sample is then determined to be the sample effect pigment.

This can be followed by at least one of steps S1510 a or S1510 b. StepS1510 a encompasses an automatic feed of the (at least one) identifiedeffect pigment (the identity of the sample effect pigment) to a controlapplication. The control application in step S1514 conduct recipeprediction or correction or search for a formula describing theformulation of the sample effect pigment. Step S1510 b encompassesdisplaying a list of matching reference distributions on a screen of adisplay device within a table of hits sorted by decreasing probabilityof classification (i.e. probability for correct identification of thesample reference pigment as the respective reference effect pigment).Step S1511 then allows a user to toggle through the hit list displayedby the display device and assess the reference effect pigments proposedto be the sample effect pigment. In step S1512, the user can then selecta suitable proposal from the hit list corresponding to receiving theinput data for selecting (at least) one of the identities. The selectedat least one sample effect pigment (or reference effect pigment,respectively) may then be fed to the control application in response to(manual) user input for further processing according to aforementionedstep S1514. In one specific example, the method may then start anew withexecution of step S1501, for example by analyzing a further sampleeffect pigment.

In the foregoing specification, embodiments have been described withreference to numerous specific details that may vary from implementationto implementation. Thus, the sole and exclusive indicator of what is,and is intended by the applicants to be, the invention is the set ofclaims that issue from this application, in the specific form in whichsuch claims issue, including any subsequent correction. Hence, nolimitation, element, property, feature, advantage or attribute that isnot expressly recited in a claim should limit the scope of such claim inany way. The specification and drawings are, accordingly, to be regardedin an illustrative rather than a restrictive sense.

The invention claimed is:
 1. A computer-implemented method for identifying an effect pigment, the method comprising executing, on at least one processor of at least one computer, steps of: a) acquiring sample image data describing a digital image of a layer comprising a sample effect pigment; b) determining, based on the sample image data, sparkle point data describing a sample distribution of sparkle points defined by the digital image, wherein each sparkle point in the sample distribution is described by three coordinates in a three-dimensional color space having a coordinate defining lightness and two coordinates defining chromaticity of the sparkle points; c) determining, based on the sparkle point data, sparkle point transformation data describing a projection of the sample distribution onto a two-dimensional color space, wherein the two-dimensional color space is defined as a chromaticity plane and the projection has a direction perpendicular to the chromaticity plane, wherein the chromaticity plane is parallel to two axes of the three-dimensional color space that define the two chromaticity coordinates and perpendicular to an axis that defines the lightness coordinate of the three-dimensional color space; d) determining, based on the sparkle point transformation data, sparkle point distribution geometry data describing a geometry of the sample distribution in the two-dimensional color space, said geometry of the sample distribution representing a geometric shape of a marginal density function of the sample distribution in the two-dimensional color space; e) acquiring a reference distribution geometry database, wherein each entry in the reference distribution geometry database comprises reference distribution geometry data describing a geometry of a reference distribution of sparkle points in the two-dimensional color space, said geometry of the reference distribution representing a geometric shape of a marginal density function of the reference distribution in the two-dimensional color space; f) acquiring a reference distribution association database, wherein each entry in the reference distribution association database describes an association between a reference distribution associated with an entry in the reference distribution geometry database and an identifier of the reference distribution, the identifier defining the identity of a reference effect pigment for which the reference distribution has been generated; g) comparing the sparkle point distribution geometry data to each entry of the reference distribution geometry database to find a reference distribution whose geometry is similar to the geometry of the sample distribution; and h) determining, using the reference distribution association data, sample pigment identity data describing an identity of the sample effect pigment, the identity of the sample pigment being the identity of the reference pigment associated with the reference distribution whose geometry is similar to the geometry of the sample distribution.
 2. The method according to claim 1, wherein the geometry of the sample distribution is elliptical and each geometry in the reference distribution geometry database is elliptical.
 3. The method according to claim 1, wherein the coordinate defining lightness defines a difference between a lightness of the respective sparkle points and a mean lightness of the sparkle points.
 4. The method according to claim 1, wherein the reference distribution whose geometry is similar to the geometry of the sample distribution is determined based on determining, from the reference distribution geometry database, the reference distribution geometry that best fits the geometry of the sample distribution of sparkle points.
 5. The method according to claim 4, wherein the reference distribution of sparkle points whose geometry best fits the geometry of the sample distribution is determined by optimizing a merit function.
 6. The method according to claim 5, wherein the merit function is defined by a difference between values of shape parameters characterizing the geometry of the reference distribution on the one hand and the values of corresponding shape parameters characterizing the geometry of the sample distribution on the other hand.
 7. The method according to claim 5, wherein the geometry of each entry in the reference distribution geometry database and of the sample distribution is elliptical and the merit function describes at least one of a distance between the centers of the ellipses describing the geometry of the reference distribution and the geometry of the sample distribution, a distance between the areas of the ellipses describing the geometry of the reference distribution and the geometry of the sample distribution, or a distance between the orientation angles of the ellipses describing the geometry of the reference distribution and the geometry of the sample distribution.
 8. A computer-implemented method of selecting an effect pigment, comprising execution of the method according to claim 1, wherein the sample pigment identity data is determined for at least two different reference distributions, and the sample identity data describes at least two possible identities of the sample effect pigment, the method further comprising executing, by the at least one processor, steps of: issuing, to an electronic display device, display data describing a list defining a prioritization of the at least two possible identities, receiving input data for selecting, based on the displayed list, one of the possible identities.
 9. The computer-implemented method of claim 8, wherein the method further comprises executing, by the at least one processor, a step of determining a recipe for the effect pigment with the selected identity on the basis of the selected identity.
 10. A non-transitory computer-readable program storage medium on which a computer program is stored, the computer program, when running on at least one processor of at least one computer or when loaded into the memory of at least one computer, causes the at least one computer to perform the method according to claim
 1. 11. At least one computer, comprising at least one processor, a computer program, and a memory, wherein the computer program, when executed by the at least one processor, causes the at least one computer to perform the method according to claim
 1. 12. A system for identifying an effect pigment, the system comprising: a) the at least one computer according to claim 11; b) at least one electronic data storage device storing at least the reference distribution geometry data and the reference distribution association data; and c) at least one digital imaging device for generating the sample image data, wherein the at least one computer is operably coupled to the at least one electronic data storage device for acquiring, from the at least one data storage device, at least one of the reference distribution data or the reference distribution association data, and to the digital imaging device for acquiring, from the digital imaging device, the sample image data.
 13. The system according to claim 11, further comprising at least one illumination source for illuminating the layer. 